The present invention relates to non-uniformity correction of imagery collected by detector arrays. More particularly, the present invention relates to scene-base non-uniformity correction (SBNUC) of imagery collected by infrared (IR) focal plane array (FPA) imaging systems.
IR imaging systems are used in a variety of demanding applications, including night vision systems to target detection systems. Such applications often require very detailed and accurate information. FPA sensors may include thousands of IR photon detectors, with different sensitivities to thermal energy and conditions. The performance of many IR imaging systems is limited by fixed pattern noise (FPN), or spatial nonuniformity in photodetector response, that is a manifestation of differences in pixel intensity, even when imaging the same amount of impinging radiation from a scene. The primary source of this FPN is attributed to the fact that each photodetector in the FPA has a differing photoresponse, due to detector-to-detector variability in the FPA fabrication process. One-time factory calibrations are mostly ineffective, because the response of each FPA photodetector changes uniquely over time, causing the FPN to slowly and randomly drift throughout sensor operation. Some sensors include larger blobs that drift together, causing even small offsets to be visible in the imagery.
Standard calibration processes remove the noise at the time of calibration, but image quality is gradually degraded between each calibration. Some sensors require frequent calibration in order to maintain an acceptable image quality. Staring IR sensors take a significant period of time to calibrate, during which time the sensor is unavailable for imaging. Though the true response of each FPA detector is nonlinear, the response is typically modelled linearly, having both a gain and bias component. Thus, under this linear assumption, the gain and bias are different for each photodetector and give rise to the nonuniformity. Techniques that seek to estimate these gain and bias (or level) parameters, and subsequently employ the estimates to remove the nonuniformity by generating correction coefficients for each detector element in the array, are known as nonuniformity correction (NUC) techniques. Source-based NUC and SBNUC techniques are the two primary classes of NUC techniques. SBNUC techniques use captured imagery of the observed scene to determine and continually update correction coefficients as required by changing conditions, in order to reduce or eliminate FPN present in the detection system.
However, such methods typically require line-of-sight (LOS) motion to distinguish real scene content from FPN. SBNUC methods do not work well when there is no scene motion, or when only part of the scene is moving relative to the sensor, as scene details may be confused with FPN resulting in image degradation. This has previously limited the applicability of SBNUC techniques to applications where the scene is always moving relative to the sensor, such as in aircraft and missile application. Previous SBNUC techniques typically employ a high pass filter to permit correction of only high frequency FPN, in an effort to minimize the impact of real scene content. High frequency scene content areas are typically identified and further processing in those areas is avoided in a given frame. These methods converge slowly to minimize the effect of temporal noise and scene content impacting FPN correcting terms. Rapid changes in input scene intensity thus result in correspondingly rapid changes in FPN, which are not fully corrected. An additional problem with these approaches is that outlying pixels may be difficult to distinguish from high frequency content and, as a result, residual FPN artifacts can be present in an image. These artifacts may look like random “spots”, “lines” and/or other visible artifacts to the end-user
Thus, what is needed is a continuous, reliable FPN correction method and system that does not degrade scene details when the scene is stationary, and which preserves the true image spatial context while removing noise.